Impact of Soil Moisture Data Assimilation on Analysis and Medium-Range Forecasts in an Operational Global Data Assimilation and Prediction System

Abstract

Accurate initial soil moisture conditions are essential for numerical weather prediction models, because they play a major role in land–atmosphere interactions. This study constructed a soil moisture data assimilation system and evaluated its impacts on the Global Data Assimilation and Prediction System based on the Korea Integrated Model (GDAPS-KIM) to improve its weather forecast skill. Soil moisture data retrieved from the Advanced Scatterometer (ASCAT) onboard the Meteorological Operational Satellite was assimilated into GDAPS-KIM using the ensemble Kalman filter method, and its impacts were evaluated for the 2019 boreal summer period. Our results indicated that the soil moisture data assimilation improved the agreement of the observations with the initial conditions of GDAPS-KIM. This led to a statistically significant improvement in the accuracy of the initial fields. A comparison of a five-day forecast against an ERA5 reanalysis and in situ observations revealed a reduction in the dry and warm biases of GDAPS-KIM over the surface and in the lower- and mid-level atmospheres. The temperature bias correction through the initialization of the soil moisture estimates from the data assimilation system was shown in the five-day weather forecast (root mean squared errors reduction of the temperature at 850 hPa by approximately 5% in East Asia).

Keywords

• land data assimilation
• soil moisture
• numerical weather prediction